Methodology for allocating resources for data quality enhancement
Communications of the ACM
Communications of the ACM
Enhancing data quality in data warehouse environments
Communications of the ACM
ICIS '97 Proceedings of the eighteenth international conference on Information systems
Database audit and control strategies
Information Technology and Management
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
Data Quality in e-Business Applications
CAiSE '02/ WES '02 Revised Papers from the International Workshop on Web Services, E-Business, and the Semantic Web
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Supporting data quality management in decision-making
Decision Support Systems
Utility-driven assessment of data quality
ACM SIGMIS Database
Journal of Management Information Systems
Impact of the Union and Difference Operations on the Quality of Information Products
Information Systems Research
Dual Assessment of Data Quality in Customer Databases
Journal of Data and Information Quality (JDIQ)
An experimental analysis of the impact of accuracy degradation in SVM classification
International Journal of Computational Intelligence Studies
Evaluating a model for cost-effective data quality management in a real-world CRM setting
Decision Support Systems
Biases in multi-criteria, satisficing decisions due to data errors
Journal of Data and Information Quality (JDIQ)
Managing Organizational Data Resources: Quality Dimensions
Information Resources Management Journal
Information Resources Management Journal
| Hi-index | 48.26 |
Most discussions of MIS's assume that the information in the records is error-free although it is recognized that errors exist. These errors occur because of delays in processing times, lengthy correction times, and, overly or insufficiently stringent data edits. In order to enable the user to implement data edits and correction procedures tailored to the degree of accuracy needed, this paper presents functional relationships between three common measures of data quality. The MIS addressed is one where records in a MIS are updated as changes occur to the record, e.g., a manpower planning MIS where the changes may relate to a serviceman's rank or skills. Since each of the updating transactions may contain an error, the transactions are subjected to various screens before the stored records are changed. Some of the transactions including some that are correct, are rejected; these are reviewed manually and corrected as necessary. In the meantime, the record is out of date and in error. Some of the transactions that were not rejected also lead to errors. The result is that at any given time the MIS record may contain errors.For each of several error control mechanisms, we show how to forecast the level of improvement in the accuracy of the MIS record if these options are implemented.